This study highlights both shared and unique barriers to health insurance coverage among rural populations, with a particular focus on adults with a history of cancer. While many barriers to coverage were consistent between the cancer-free rural population and those with a history of cancer, important distinctions emerged that underscore the complex needs of cancer survivors and patients in rural areas.
4.1 Reasons for uninsurance among adults with and without a history of cancer
A higher proportion of individuals with a history of cancer reported that “available plans don’t meet patient needs.” This likely reflects the complex and multifaceted care pathways required in oncology, where patients often need access to services across multiple healthcare systems and subspecialties. Insurance plans in rural areas are often limited in number and network breadth, as shown in previous research documenting higher premiums and fewer plan options in rural markets [23]. Consequently, rural cancer survivors may encounter more frequent gaps in coverage for the diverse provider needs or special facilities involved in their care, such as tertiary centers located outside of their insurance plan network [24]. Additionally, these patients may require specialized medications or treatment, regular imaging, or supportive services not adequately covered under basic or high-deductible plans that are commonly available in rural insurance markets [23].
Cost was a significant barrier across both groups. Financial toxicity—the burden of healthcare-related costs—has been well documented in prior rural cancer care research [25,26,27], and our results indicate that, especially among the uninsured, financial concerns are a central reason for forgoing coverage. This underscores the need for affordable insurance options that accommodate both the general and disease-specific needs of rural populations.
Interestingly, a large proportion of subjects in both groups selected “some other reason” for being uninsured. This suggests that there are additional, unexplored drivers of uninsurance that fall outside of typical economic or logistical explanations. Future qualitative research will be vital in exposing these nuances, and should particularly explore social, cultural, psychological, or systemic deterrents to coverage that are not easily captured in survey-based research.
4.2 Reasons for uninsurance patterns along sociodemographic strata
Those who cited “job loss” and “too expensive” were more likely to be White non-Hispanic. This initially surprising trend likely reflects different baseline health insurance sources between racial and ethnic groups; White non-Hispanic individuals have higher rates of employer-sponsored coverage than other groups [28] (except for Asian non-Hispanic individuals, who are some of the least likely to live in rural areas), and minoritized groups are more likely to be on public insurance, so when White non-Hispanic individuals lose health insurance coverage, it is more likely to be because of a job loss. Income is directly tied to employment, and it makes sense that income and “too expensive” would follow the same trends as “job loss.”
Those who cited “too expensive” were more likely to be older. Although counterintuitive at first glance, our results align with previous research on health insurance expense trends among middle-aged adults. In our sample, the oldest adults are still under 65; the typical Medicare eligibility age. These middle-aged adults are typically more likely to be insured than younger adults [29]; however, their premiums also rise, as their families expand and they must pay for their children’s health insurance, and the likelihood of comorbid conditions (which are associated with higher health insurance costs) increases over time. Indeed, a 50-year-old has a federal premium multiplier twice as high as a 21-year-old [30]. Additionally, those who cited “ineligible for coverage” were more likely to be younger. This is likely partially due to the aging-out cliff, where younger adults age out of their parents’ health insurance coverage at age 26 (the largest group citing this reason was adults ages 26–34), although we still observed that the ages 18–25 group was the second-largest group to cite ineligibility as a factor in their uninsurance. This could be caused by dropping out of college and losing student health plans or aging out of CHIP (at age 18). Rural areas benefit dramatically from Medicaid expansion status [31], and our results support expanding Medicaid regarding CHIP.
Female gender was associated with a higher likelihood of citing “too expensive” and “ineligible for coverage.” It is well known that women have higher healthcare costs than men; approximately 18% more as of 2015 [32], despite women typically having lower rates of uninsurance than men [33]. Rural women also face higher rates of under- and uninsurance compared to urban women, as evidenced by previous work examining pre-pregnancy, pregnancy, and postpartum coverage [34]. Our results add to this current body of knowledge and suggest that ineligibility under current policies plays an additional role in under- and uninsurance among rural women.
Those who cited “job loss” were more likely to have lower educational attainment. Health insurance is strongly correlated with educational attainment [35]; one study describes 94% coverage among individuals with Bachelor’s degrees or higher compared to 67% among those without a high school education [36]. Those with college degrees also fare better during recessions in terms of reductions in force and job losses than those without [37]. Our findings support that educational attainment and employment are correlated with health insurance.
Lower household income was associated with citing “too expensive” and “ineligible for coverage.” While the correlation between lower incomes and “too expensive” as a reason is readily apparent and extremely plausible, the correlation between lower incomes and ineligibility is less obvious; Medicaid eligibility, in particular, is income-based. Likely driving this trend are individuals with low incomes that are just high enough to be ineligible for Medicaid; these individuals, particularly non-disabled adults without children, often fall through eligibility gaps. Those who cited ‘job loss’ were more likely to have a higher household income. This is likely because individuals with lower household incomes are on Medicaid or other public insurance, and do not get their insurance through their jobs; therefore, a job loss would not affect health insurance status for individuals from lower household incomes.
Higher rates of comorbid conditions were associated with a higher likelihood of citing “Available insurance plans don’t meet patient needs” and “Too expensive.” This points to the increased healthcare needs of medically complex patients, consistent with previous research from the early 2020 s describing how patients managing chronic illnesses have more barriers to care, including higher out-of-pocket costs compared to patients without chronic illnesses [38].
Finally, those who cited “Some other reason” were more likely to be non-White non-Hispanic and have lower educational attainment, indicating that these particular rural groups may experience drivers of uninsurance outside of typical economic explanations. Future research should investigate reasons for uninsurance among these two groups, both within and beyond rurality.
When taken alongside these described findings, our earlier findings concerning reasons for uninsurance among cancer historied vs. no cancer historied rural working age adults suggest that uninsurance in these populations operates through heterogeneous and partially overlapping pathways rather than a single underlying mechanism. Socioeconomic factors strongly shape both exposure to and interpretation of insurance barriers, as evidenced by a myriad of previous research and by strong associations between sociodemographic covariates and several investigated reasons for uninsurance. Yet, these socioeconomic gradients do not fully explain the differences observed by cancer history. Individuals with a history of cancer were more likely to report that available plans did not meet their needs, suggesting that clinical complexity and the structure of rural insurance markets jointly influence perceptions of coverage adequacy. Importantly, these patterns highlight that “reasons for uninsurance” reflect not only objective determinants of coverage status, but also how individuals experience and interpret gaps in insurance relative to their healthcare needs.
4.3 Implications for policy and practice
Our results show that rural cancer-historied adults are more likely to report that available health insurance plans do not meet their needs, but we did not investigate specific drivers of this gap (for example, limited provider network, less telehealth access, and travel burden, all known issues in rural America). Future research should examine various elements of insurance plans currently available in rural areas, and how they correlate with a decision to purchase, particularly among individuals with serious or chronic health conditions. We can also begin to hypothesize several potential beneficial interventions (although, as stated earlier, we cannot discern from this current study what the largest drivers of the unmet health insurance plan needs gap are). Firstly, incentivizing insurers to expand rural networks through marketplace reforms or targeted subsidies may help significantly increase insurance coverage in the population of interest (rural cancer patients and survivors), should a driver be the unavailability of insurance plans. Insurance plans that include broader networks and cover care across geographically dispersed providers would also address one of the main concerns raised by rural cancer survivors and health services researchers in previous research: intense centralization of cancer care. Additionally, improving healthcare literacy through community-based education programs may help address knowledge-related barriers to insurance coverage, particularly in areas with limited access to digital resources. Finally, bolstering broadband infrastructure could further mitigate access barriers by facilitating online enrollment and telehealth services. All of these things could conceivably lead to improvements in insurance coverage rates. We acknowledge that with most potential benefits come potential drawbacks; for instance, while requiring a broader network would presumably increase access to health insurance plans, this may also raise overall premiums for enrollees and could actually lead to less enrollment in the overall population due to rising health insurance costs. We reiterate the need for further research on drivers of decisions around insurance plan purchasing in rural and health condition-specific populations.
This study also brings to light significant implications for care delivery systems, particularly the current centralized nature of cancer care delivery systems. Integrated care models that coordinate oncology care across different facilities could be better supported through alternative payment models and value-based arrangements that account for rural patients’ travel burdens and fragmented care environments. These innovations would need to be paired with patient navigation services to ensure rural cancer survivors can effectively engage with complex care systems and insurance processes. This example has been exhibited in a recent ASCO pilot study in Dillon, Montana, which demonstrated significant patient benefits when providing high-quality care within a network system closer to home [39, 40]. It has also been exhibited in a recent financial navigation intervention scaled to eight sites, although this was not without significant workforce and administrative barriers [41].
4.4 Limitations and future directions
There are several limitations to our study. Firstly, we were not able to distinguish between types of cancer, nor between those currently undergoing cancer treatments versus those months to years removed from active treatment. This distinction may be critical, as current treatment status could influence factors such as employment status or acute financial burden, both of which may differ depending on treatment stage [42, 43]. Additionally, the cross-sectional nature of the data limits causal inference, and responses may be subject to recall bias, particularly among cancer survivors who may have heightened awareness of insurance and financial issues. Future research should be either qualitative or mixed-method in nature to elucidate important mechanisms contributing to insurance barriers for individuals diagnosed with cancer. The operationalization of “rural” as a binary variable also constrains our ability to detect variation across different subtypes of rural communities; we encourage future research to consider the use of more granular geographic classifications, such as rural-urban commuting areas (RUCA), rural-urban continuum codes (RUCC), urban influence codes (UIC), frontier and remote area codes (FAR), ERS typology, or natural amenities scales [44]. Finally, the small sample size of the case group (N = 91) limits analytical power, particularly among subgroups such as race and ethnicity; however, the data is survey-weighted to the national level and therefore generalizable, and the robustness of our findings in sensitivity analyses lends additional confidence to the observed trends.
Other future directions of research include performing similar analyses among urban residents, particularly among underserved urban populations (we note that urban residents are readily available in NHIS 2020–2024); examining rates of these issues among rural vs. urban residents; and investigating relationships between uninsurance and rurality by cancer type, number of diagnoses, and time since diagnosis.
